At the Geostationary Earth Orbit (GEO), routine observations lack the resolution to reveal physical characteristics, especially for smaller, discarded objects. To address this, we introduce a new classification system that succinctly characterises GEO objects by their control, regional residence and coplanarity to detect drifters. Applied over the entire catalogue from 1963 to 2025, the data shows that 36.3% of uncontrolled objects reside within the protected region containing operational satellites, with each 1◦ slot hosting 1.2 drifters on average, underscoring the value of routinely tracking uncontrolled traffic at GEO. To estimate the otherwise unobservable physical parameters (illuminated area and mass) of these drifting objects, we present an improved Two-Line

Element (TLE) catalogue.

To infer area and mass, a two-stage, coarse-to-fine parameter search is coupled to a High Precision Orbital Propagator (HPOP) and robust z-score fit metrics scaled by IQR. Matching the history of the eccentricity ground truth to the propagated result at various area-mass pairs yields an informed approximation. To validate an eccentricity-only approach, we show eccentricity fit errors dominate the dispersion, with the median within-object IQR ratios of 80.4 versus semi-major axis and 6.4 versus position-magnitude errors. The area and mass pair selected by minimising the eccentricity error was Pareto-optimal when compared against both position and semi-major axis errors and confirms our eccentricity-driven strategy is sufficient to infer illuminated area and

mass.

We also observe time-varying area-to-mass ratio, consistent with solar radiation pressure aliasing of rotational dynamics reported for high area-to-mass ratio debris.Drifting satellites with maximum observation cadence >5 days have on average 24.1% higher mean absolute position error, and beyond this threshold the error grows by 11.6% per additional unobserved day. Duplicate measurements at the initial epoch inflate the error by 23.9% for drifters on average. These statistics quantify how data hygiene and observation frequency directly influence poorer area/mass estimates by polluting our reference ground truth. The results indicate that regularised cadence, informed datasets with physical characteristics and clean initial conditions for GEO Protected Region (GEOPR) drifters can reduce position error by 92.2%. In conclusion, our update to the TLE catalogue with parameters and filters will enhance trajectory propagation and therefore risk forecasting at GEO.